從2014年九合一選舉到今年總統大選，網路在選戰的影響度越來越大，後選人可透過網路上之熱門討論議題即時掌握民眾需求。 文字情感分析通常使用監督式或非監督式的方法來分析文件，監督式透過文件量化可達很高的正確率，但無法預期未知趨勢，耗費人力標注文章。 本研究針對網路上之政治新聞輿情，提出一個混合非監督式與監督式學習的中文情感分析方法，先透過非監督式方法標注新聞，再用監督式方法建立分類模型，驗證分類準確率。 在實驗結果中，主題標注方面，本研究發現因文本數量遠大於議題詞數量造成TFIDF矩陣過於稀疏，使得TFIDF-Kmeans主題模型分類效果不佳；而NPMI-Concor主題模型分類效果較佳但是所分出的議題詞數量不均衡，然而LDA主題模型基於所有主題被所有文章共享的特性，使得在字詞分群與主題分類準確度都優於TFIDF-Kmeans和NPMI-Concor主題模型，分類準確度高達97%，故後續採用LDA主題模型進行主題標注。 情緒傾向標注方面，證實本研究擴充後的情感詞集比起NTUSD有更好的字詞極性判斷效果，並且進一步使用ChineseWordnet 和 SentiWordNet，找出詞彙的情緒強度，使得在網友評論的情緒計算更加準確。亦發現所有文本的情緒指數皆具皆能反應民調指數，故本研究用文本的情緒指數來建立民調趨勢分類模型。 在關注議題分類結果的實驗，整體正確率達到95%，而在民調趨勢分類結果的實驗，整體正確率達到85%。另外建立全面性的視覺化報告以瞭解民眾的正反意見，提供候選人在選戰上之競爭智慧。 / From Taiwanese local elections, 2014 to Taiwan presidential elections, 2016. Network is in growing influence of the election. The nominee can immediately grasp the needs of the people through a popular subject of discussion on the website. Sentiment Analysis research encompasses supervised and unsupervised methods for analyzing review text. The supervised learning is proved as a powerful method with high accuracy, but there are limits where future trend cannot be recognized, and the labels of individual classes must be made manually. In the study, we propose a Chinese Sentiment Analysis method which combined supervised and unsupervised learning. First, we used unsupervised learning to label every articles. Secondly, we used supervised learning to build classification model and verified the result. According to the result of finding subject labeling, we found that TFIDF-Kmeans model is not suitable because of document characteristic. NPMI-Concor model is better than TFIDF-Kmeans model. But the subject words is not balanced. However, LDA model has the feature that all subject is share by all articles. LDA model classification performance can reach 97% accuracy. So we choose it to decide article subject. According to the result of sentimental labeling, the sentimental dictionary we build has higher accuracy than NTUSD on judging word polarity. Moreover, we used ChineseWordnet and SentiWordNet to calculate the strength of word. So we can have more accuracy on calculate public’s sentiment. So we use these sentiment index to build prediction model. In the result of subject labeling, our accuracy is 95%. Meanwhile, In the result of prediction our accuracy is 85%. We also create the Visualization report for the nominee to understand the positive and the negative options of public. Our research can help the nominee by providing competitive wisdom.

全球智慧型手機的出貨量持續提升，且熱門市集的App下載次數紛紛突破500億次。而在iOS和Android手機App市集中，App的評價和評論對App在市集的排序有很大的影響；對於App開發者而言，透過評論確實可掌握使用者的需求，並在產生抱怨前能快速反應避免危機。然而，每日多達上百篇的評論，透過人力逐篇查看，不止耗費時間，更無法整合性的瞭解使用者的需求與問題。 文字情感分析通常會使用監督式或非監督式的方法分析文字評論，其中監督式方法被證實透過簡單的文件量化方法就可達到很高的正確率。但監督式方法有無法預期未知趨勢的限制，且需要進行耗費人力的文章類別標注工作。 本研究透過情感傾向和熱門關注議題兩個面向來分析App評論，提出一個混合非監督式與監督式的中文情感分析方法。我們先透過非監督式方法標注評論類別，並作視覺化整理呈現，最後再用監督式方法建立分類模型，並驗證其效果。 在實驗結果中，利用中文詞彙網路所建立的情感詞集，確實可用來判斷評論的正反情緒，唯判斷負面評論效果不佳需作改善。在議題擷取方面，嘗試使用兩種不同分群方法，其中使用NPMI衡量字詞間關係強度，再配合社群網路分析的Concor方法結果有不錯的成效。最後在使用監督式學習的分類結果中，情感傾向的分類正確率達到87%，關注議題的分類正確率達到96%，皆有不錯表現。 本研究利用中文詞彙網路與社會網路分析，來發展一個非監督式的中文類別判斷方法，並建立一個中文情感分析的範例。另外透過建立全面性的視覺化報告來瞭解使用者的正反回饋意見，並可透過分類模型來掌握新評論的內容，以提供App開發者在市場上之競爭智慧。 / While the number of smartphone shipment is continuesly growing, the number of App downloads from the popular app markets has been already over 50 billion. By Apple App Store and Google Play, ratings and reviews play a more important role in influencing app difusion. While app developers can realize users’ needs by app reviews, more than thousands of reviews produced by user everday become difficult to be read and collated. Sentiment Analysis researchs encompass supervised and unsupervised methods for analyzing review text. The supervised learning is proven as a useful method and can reach high accuracy, but there are limits where future trend can not be recognized and the labels of individual classes must be made manually. We concentrate on two issues, viz Sentiment Orientation and Popular Topic, to propose a Chinese Sentiment Analysis method which combines supervised and unsupervised learning. At First, we use unsupervised learning to label every review articles and produce visualized reports. Secondly, we employee supervised learning to build classification model and verify the result. In the experiment, the Chinese WordNet is used to build sentiment lexicon to determin review’s sentiment orientation, but the result shows it is weak to find out negative review opinions. In the Topic Extraction phase, we apply two clustering methods to extract Popular Topic classes and its result is excellent by using of NPMI Model with Social Network Analysis Method i.e. Concor. In the supervised learning phase, the accuracy of Sentiment Orientation class is 87% and the accuracy of Popular Topic class is 96%. In this research, we conduct an exemplification of the unsupervised method by means of Chinese WorkNet and Social Network Analysis to determin the review classes. Also, we build a comprehensive visualized report to realize users’ feedbacks and utilize classification to explore new comments. Last but not least, with Chinese Sentiment Analysis of this research, and the competitive intelligence in App market can be provided to the App develops.